Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening

HIGHLIGHTS

• who: . and colleagues from the Oregon State University, United States have published the article: Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening, in the Journal: (JOURNAL)
• what: The authors develop (Q)SAR models to predict endocrine receptor activity, specifically estrogen and androgen receptor activity, to cover a broader range of substances than previous models, aiming to cover more of the Canadian DSL while maintaining high predictive capabilities. In contrast, the original CERAPP Evaluation data, which was used for training the current model, was in_vitro data collected through literature and . . .
  

   

  

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